# Accent Recognition with Hybrid Phonetic Features

arXiv: https://arxiv.org/abs/2105.01920 · HTML: https://mlsanity.com/paper/2105.01920/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 May 2021.

## Built on by (most cited first, 3 of 3)

- [What Can an Accent Identifier Learn? Probing Phonetic and Prosodic Information in a Wav2vec2-based Accent Identification Model](https://mlsanity.com/paper/2306.06524/) (2023)
- [Learning Nigerian accent embeddings from speech: preliminary results based on SautiDB-Naija corpus](https://mlsanity.com/paper/2112.06199/) (2021)
- [TIMIT Speaker Profiling: A Comparison of Multi-task learning and Single-task learning Approaches](https://mlsanity.com/paper/2404.12077/) (2024)

## Abstract (arXiv)

The performance of voice-controlled systems is usually influenced by accented speech. To make these systems more robust, the frontend accent recognition (AR) technologies have received increased attention in recent years. As accent is a high-level abstract feature that has a profound relationship with the language knowledge, AR is more challenging than other language-agnostic audio classification tasks. In this paper, we use an auxiliary automatic speech recognition (ASR) task to extract language-related phonetic features. Furthermore, we propose a hybrid structure that incorporates the embeddings of both a fixed acoustic model and a trainable acoustic model, making the language-related acoustic feature more robust. We conduct several experiments on the Accented English Speech Recognition Challenge (AESRC) 2020 dataset. The results demonstrate that our approach can obtain a 6.57% relative improvement on the validation set. We also get a 7.28% relative improvement on the final test set for this competition, showing the merits of the proposed method.